A conventional fingerprint verification method adopted the characteristics of branch points, end points and curvature as parameters for comparison. Image binarization and thinning with centering were prerequisites for this comparison. The positioning of the sample image and the master image were also prerequisites so that corresponding points of the two images fit each other. In the conventional method, the characteristic points in both images were considered the standards and it was necessary to extract the characteristics clearly. Therefore conventionally, verification accuracy was poor because of the bad quality of an image, that is, a pinched part or a broken part of the ridge, or noise. That is to say, the conventional fingerprint verification was easily influenced by micro data of characteristics and the condition was strict for inputting the image.